Freeway: Adaptively Isolating the Elephant and Mice Flows on Different Transmission Paths

The network resource competition of today' data enters is extremely intense between long-lived elephant flows and latency-sensitive mice flows. Achieving both goals of high throughput and low latency respectively for the two types of flows requires compromise, which recent research has not successfully solved mainly due to the transfer of elephant and mice flows on shared links without any differentiation. However, current data enters usually adopt clos-based topology, e.g. Fat-tree/VL2, so there exist multiple shortest paths between any pair of source and destination. In this paper, we leverage on this observation to propose a flow scheduling scheme, Freeway, to adaptively partition the transmission paths into low latency paths and high throughput paths respectively for the two types of flows. An algorithm is proposed to dynamically adjust the number of the two types of paths according to the real-time traffic. And based on these separated transmission paths, we propose different flow type-specific scheduling and forwarding methods to make full utilization of the bandwidth. Our simulation results show that Freeway significantly reduces the delay of mice flow by 85.8% and achieves 9.2% higher throughput compared with Hedera.